Vision-based Automated Behavior Recognition

Automated quantitative analysis of animal behavior is an important tool for assessing the effects of diseases, drugs, gene mutations, and perturbations in neural circuits. Automating behavioral analysis addresses the inherent limitations of human assessment (time, cost, and reproducibility) and allows researchers to study behaviors over longer time scales than typically studied.
Developed from a computational model of motion processing in the primate visual cortex, we built a computer vision system to recognize a typical set of 8 behaviors of single mice in the home cage (see http://cbcl.mit.edu/software-datasets/hueihan/). In ongoing work, our goal is to significantly extend this system in two major directions:

1. Recognition of more behaviors in multiple mice (such as social behaviors)

Throughout the process, we maintain a commitment to release these behavioral tools publicly to the academic research community.

Starting in IAP 2011, the Center for Biological and Computation Learning (CBCL) will have a position available for a MEng student to be in charge of extending this system. We expect that the research, as in our past work, will combine computer vision and machine learning.

Prerequisites: The ideal student will have experience and research interests in machine learning and computer vision complemented by very strong programming skills (Matlab, C++). We work closely with other research collaborators to develop these systems, so a passion for design and usability is a plus.